论文标题
血管的分割，视盘定位，渗出液检测和糖尿病性视网膜病变诊断数字眼底图像
Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
论文作者
论文摘要
糖尿病性视网膜病（DR）是长期存在的，未经检查的糖尿病的并发症，是世界上失明的主要原因之一。本文着重于改进且可靠的方法来提取DR，VIZ的某些特征。血管和渗出液。使用多个形态学和阈值操作对血管进行分割。对于渗出液的分割，使用了原始图像上的K均值聚类和轮廓检测。进行了广泛的降噪，以从血管分割算法的结果中删除误报。还执行了使用K-均值聚类和模板匹配的光盘定位。最后，本文提出了一个深卷积神经网络（DCNN）模型，该模型具有14个卷积层和2个完全连接的层，用于自动二元诊断。血管分割，视盘定位和DCNN的精度分别为95.93％，98.77％和75.73％。源代码和预培训模型可用https://github.com/sohambasu07/dr_2021
Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and thresholding operations. For the segmentation of exudates, k-means clustering and contour detection on the original images are used. Extensive noise reduction is performed to remove false positives from the vessel segmentation algorithm's results. The localization of Optic Disc using k-means clustering and template matching is also performed. Lastly, this paper presents a Deep Convolutional Neural Network (DCNN) model with 14 Convolutional Layers and 2 Fully Connected Layers, for the automatic, binary diagnosis of DR. The vessel segmentation, optic disc localization and DCNN achieve accuracies of 95.93%, 98.77% and 75.73% respectively. The source code and pre-trained model are available https://github.com/Sohambasu07/DR_2021
